docs: Mastra integration

qdrant-landing/content/documentation/frameworks/_index.md

@@ -25,6 +25,7 @@ partition: build
 | [Langchain4j](/documentation/frameworks/langchain4j/)         | Java framework for building context-aware, reasoning applications using LLMs.                                        |
 | [LangGraph](/documentation/frameworks/langgraph/)             | Python, Javascript libraries for building stateful, multi-actor applications.                                        |
 | [LlamaIndex](/documentation/frameworks/llama-index/)          | A data framework for building LLM applications with modular integrations.                                            |
+| [Mastra](/documentation/frameworks/mastra/)                   | Typescript framework to build AI applications and features quickly.                                                  |
 | [Mem0](/documentation/frameworks/mem0/)                       | Self-improving memory layer for LLM applications, enabling personalized AI experiences.                              |
 | [MemGPT](/documentation/frameworks/memgpt/)                   | System to build LLM agents with long term memory & custom tools                                                      |
 | [Neo4j GraphRAG](/documentation/frameworks/neo4j-graphrag/)   | Package to build graph retrieval augmented generation (GraphRAG) applications using Neo4j and Python.                |

qdrant-landing/content/documentation/frameworks/mastra.md

@@ -0,0 +1,104 @@
+---
+title: Mastra
+---
+
+# Mastra
+
+[Mastra](https://mastra.ai/) is a Typescript framework to build AI applications and features quickly. It gives you the set of primitives you need: workflows, agents, RAG, integrations, syncs and evals. You can run Mastra on your local machine, or deploy to a serverless cloud.
+
+Qdrant is available as a vector store in Mastra node to augment application with retrieval capabilities.
+
+## Setup
+
+```bash
+npm install @mastra/core
+```
+
+## Usage
+
+```typescript
+import { QdrantVector } from "@mastra/rag";
+
+const qdrant = new QdrantVector({
+  url: "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333"
+  apiKey: "<YOUR_API_KEY>",
+  https: true
+});
+```
+
+## Constructor Options
+
+| Name   | Type      | Description                                                                                           |
+|--------|-----------|-------------------------------------------------------------------------------------------------------|
+| `url`  | `string`  | REST URL of the Qdrant instance. Eg. <https://xyz-example.eu-central.aws.cloud.qdrant.io:6333>         |
+| `apiKey` | `string` | Optional Qdrant API key                                                                              |
+| `https` | `boolean` | Whether to use TLS when setting up the connection. Recommended.                                     |
+
+## Methods
+
+### `createIndex()`
+
+| Name       | Type                                     | Description                                     | Default Value |
+|------------|------------------------------------------|-------------------------------------------------|--------------|
+| `indexName` | `string`                                | Name of the index to create                     |              |
+| `dimension` | `number`                                | Vector dimension size                           |              |
+| `metric`    | `string`                                | Distance metric for similarity search           | `cosine`     |
+
+### `upsert()`
+
+| Name        | Type                      | Description                             | Default Value |
+|-------------|---------------------------|-----------------------------------------|--------------|
+| `vectors`   | `number[][]`              | Array of embedding vectors              |              |
+| `metadata`  | `Record<string, any>[]`   | Metadata for each vector (optional)     |              |
+| `namespace` | `string`                  | Optional namespace for organization     |              |
+
+### `query()`
+
+| Name       | Type                    | Description                                 | Default Value |
+|------------|-------------------------|---------------------------------------------|--------------|
+| `vector`   | `number[]`              | Query vector to find similar vectors       |              |
+| `topK`     | `number`                | Number of results to return (optional)     | `10`         |
+| `filter`   | `Record<string, any>`   | Metadata filters for the query (optional)  |              |
+
+### `listIndexes()`
+
+Returns an array of index names as strings.
+
+### `describeIndex()`
+
+| Name        | Type     | Description                      |
+|-------------|----------|----------------------------------|
+| `indexName` | `string` | Name of the index to describe    |
+
+#### Returns
+
+```typescript
+interface IndexStats {
+  dimension: number;
+  count: number;
+  metric: "cosine" | "euclidean" | "dotproduct";
+}
+```
+
+### `deleteIndex()`
+
+| Name        | Type     | Description                      |
+|-------------|----------|----------------------------------|
+| `indexName` | `string` | Name of the index to delete      |
+
+## Response Types
+
+Query results are returned in this format:
+
+```typescript
+interface QueryResult {
+  id: string;
+  score: number;
+  metadata: Record<string, any>;
+}
+```
+
+## Further Reading
+
+- [Mastra Examples](https://github.com/mastra-ai/mastra/tree/main/examples)
+- [Mastra Documentation](http://mastra.ai/docs/)